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Record W2521940220 · doi:10.1158/1940-6215.prev-13-a42

Abstract A42: Intake of vitamins A, C, E, and folate and risk of ovarian cancer in a pooled analysis of 10 cohort studies

2013· article· en· W2521940220 on OpenAlexaffabout
Anita Koushik, Stephanie A. Smith‐Warner

Bibliographic record

VenueCancer Prevention Research · 2013
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineCohortCohort studyNurses' Health StudyBreast cancerCancerCancer preventionEnvironmental healthOvarian cancerEpidemiologyRelative riskGynecologyOncologyInternal medicineGerontologyConfidence interval

Abstract

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Abstract Vitamins A, C, E and folate have properties, such as modulation of DNA synthesis and repair, control of cellular differentiation and proliferation, as well as antioxidation, which are potentially cancer preventive. In a 2007 international panel review of the epidemiological literature published through 2006, the available data on the associations between intake of these vitamins and ovarian cancer risk were judged to be limited and inconclusive. Relatively few studies had been published and statistical power may have been limited in most studies. Among subsequent studies, sample sizes have been large in some though results remain inconsistent. In this project, we examined vitamin intakes from food only (dietary) and from food and supplements together (total) in relation to ovarian cancer risk by pooling the primary data from the following studies: Breast Cancer Detection Demonstration Project Follow-up Study, Canadian National Breast Screening Study, Cancer Prevention Study II Nutrition Cohort, Iowa Women's Health Study, Netherlands Cohort Study, New York State Cohort, New York University Women's Health Study, Nurses' Health Study, Nurses' Health Study II, and Swedish Mammography Cohort. Vitamin intakes were ascertained from a validated food frequency questionnaire administered at baseline in each study. Study-specific relative risks (RR) were estimated using the Cox proportional hazards model, and then combined using a random-effects model. Multivariate models included total energy intake and other potential ovarian cancer risk factors. Among 501,857 women, 1,973 cases of ovarian cancer occurred during a maximum follow-up of 7 to 22 years across studies. When analyzed as continuous variables the RRs for dietary and total intakes of each of the vitamins were not significantly associated with ovarian cancer. For increments of intake defined by the mean of the standard deviation of the mean intake across studies, the pooled multivariate RRs (95% CI) for total intake of each vitamin were 1.02 (0.97-1.07) for each 1300 mcg/day increase in vitamin A, 1.01 (0.99-1.04) for each 400 mg/day increase in vitamin C, 1.02 (0.97-1.06) for each 130 mg/day increase in vitamin E and 1.01 (0.96-1.07) for each 250 mcg/day increase in folate. When vitamin intakes were analyzed as categorical variables defined by study-specific quintiles of intake, the results were consistent with the continuous analyses and indicated no significant association. There was no evidence of statistically significant heterogeneity between studies in any of the analyses. Also, the pooled RRs did not vary greatly by levels of parity, oral contraceptive use, postmenopausal hormone use, smoking status or alcohol consumption, nor did associations greatly differ by histological type. We also examined use of specific vitamin supplements and multivitamins and did not observe a significant association with risk of ovarian cancer overall; the pooled multivariate RR (95% CI) for multivitamin use versus non-use was 1.00 (0.89-1.12). This large pooled analysis suggests that vitamins A, C, E and folate are not associated with the risk of ovarian cancer. Citation Format: Anita Koushik, Stephanie A. Smith-Warner. Intake of vitamins A, C, E, and folate and risk of ovarian cancer in a pooled analysis of 10 cohort studies. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr A42.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.444
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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